{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three Blobs Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function that loads the `three_blobs` dataset into NumPy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.data import three_blobs_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A random dataset of 3 2D blobs for clustering.\n",
    "\n",
    "- Number of samples : 150\n",
    "- Suggested labels $\\in$ {0, 1, 2}, distribution: [50, 50, 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Dataset overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions: 150 x 2\n",
      "1st row [ 2.60509732  1.22529553]\n"
     ]
    }
   ],
   "source": [
    "from mlxtend.data import three_blobs_data\n",
    "X, y = three_blobs_data()\n",
    "\n",
    "print('Dimensions: %s x %s' % (X.shape[0], X.shape[1]))\n",
    "\n",
    "print('1st row', X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suggested cluster labels\n",
      "[0 1 2]\n",
      "Label distribution: [50 50 50]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print('Suggested cluster labels')\n",
    "print(np.unique(y))\n",
    "print('Label distribution: %s' % np.bincount(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nrJfalbKR0QeuJiAmA0XIiY5Ov1p64IEHsr53XV2dbN++PS/vm8tkq1L7Y15s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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105e6d4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1],\n",
    "            c='white',\n",
    "            marker='o',\n",
    "            s=50)\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tez0FtXCmRTDiTzo4q2MgoETMvict5NY+cIfaho7eZh/8BX96P/QIGQMEQZhR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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105f12748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y == 0, 0],\n",
    "            X[y == 0, 1],\n",
    "            s=50,\n",
    "            c='lightgreen',\n",
    "            marker='s',\n",
    "            label='cluster 1')\n",
    "\n",
    "plt.scatter(X[y == 1,0],\n",
    "            X[y == 1,1],\n",
    "            s=50,\n",
    "            c='orange',\n",
    "            marker='o',\n",
    "            label='cluster 2')\n",
    "\n",
    "plt.scatter(X[y == 2,0],\n",
    "            X[y == 2,1],\n",
    "            s=50,\n",
    "            c='lightblue',\n",
    "            marker='v',\n",
    "            label='cluster 3')\n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## three_blobs_data\n",
      "\n",
      "*three_blobs_data()*\n",
      "\n",
      "A random dataset of 3 2D blobs for clustering.\n",
      "\n",
      "\n",
      "- `Number of samples` : 150\n",
      "\n",
      "\n",
      "- `Suggested labels` : {0, 1, 2}, distribution: [50, 50, 50]\n",
      "\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `X, y` : [n_samples, n_features], [n_cluster_labels]\n",
      "\n",
      "    X is the feature matrix with 159 samples as rows\n",
      "    and 2 feature columns.\n",
      "    y is a 1-dimensional array of the 3 suggested cluster labels 0, 1, 2\n",
      "\n",
      "**Examples**\n",
      "\n",
      "For usage examples, please see\n",
      "    [http://rasbt.github.io/mlxtend/user_guide/data/three_blobs_data](http://rasbt.github.io/mlxtend/user_guide/data/three_blobs_data)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.data/three_blobs_data.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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